A Pure Data-Driven Method for Online Inertia Estimation in Power Systems Using Local Rational Model Approach

Abstract

This paper presents an online data-driven method to estimate the inertia constant of synchronous generators (SGs) and the virtual inertia of converter-based resources (CBRs), which enables time-dependent inertia tracking in the normal operation of a power system. The proposed method is based on continuous monitoring of frequency response functions (FRFs) of SGs and CBRs, which are identified by ambient wide-area measurements of phasor measurement units (PMUs). To identify FRFs, a novel non-parametric approach, namely the local rational model (LRM), is used which does not require correct model order selection. LRM approach has a low computational burden and requires a short window of data, both of which are essential for estimating time-dependent inertia using ambient data. The applicability of the proposed method is evaluated in the IEEE 39-bus system and an actual system. The results demonstrate the accuracy, robustness to noise, and effectiveness of the proposed method in estimating the time-dependent inertia of power systems.